Co-channel interference cancellation with multiple receive antennas for BICM

ABSTRACT

Apparatus and methods are provided for computing soft information at a receiver having a plurality of receive antennas. The receiver may be a mobile station or a base station, and can receive a signal vector that includes an intended signal from a first source as well as interfering signals from one or more other, interfering sources. The receiver can determine modulation information, such as the modulation scheme, used by each of the interfering sources. The mobile station can also estimate channel information, such as channel gain information, associated with each interfering source. Using the modulation and channel information, the receiver can compute soft information, such as a log-likelihood ratio. In some embodiments, the receiver can adaptively determine which interference sources and which receive antennas to use when computing the soft information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This claims the benefit under 35 U.S.C. §119(e) of U.S. ProvisionalApplication No. 60/950,425, filed Jul. 18, 2007. This is also related toU.S. patent application Ser. No. 12/119,264, filed May 12, 2008. Theseprior applications are hereby incorporated by reference herein in theirentirety.

BACKGROUND OF THE DISCLOSURE

The disclosed technology relates generally to decoding received signals,and more particular to computing soft information for signals receivedfrom an intended source in the presence of interference from othersources.

There are several known wireless protocols for cellular and Internetsystems. These wireless protocols attempt to provide high transmissionreliability to wireless users, such as cellular telephone users, toprevent dropped telephone calls or poor voice transmissions. Forexample, to reduce the effect of interfering signals, the Global Systemsfor Mobile communications (“GSM”) protocol decomposes the frequency bandallocated for cellular communication into seven frequency channels. Thisallows a cellular telephone to tune into only the appropriate channel toavoid interfering signals that are transmitted through the other sixchannels. However, such a communications technique forces datatransmission to occur at a fraction of the maximum possible bandwidth.Reducing the bandwidth in this manner limits the maximum data rate thatcan be achieved by a communications network.

For conventional systems that utilize multiple receive antennas,interference cancellation is attempted by using matched filtering orlinear equalization, such as zero-forcing or minimum-mean squared errorequalization. Such techniques, however, may not be effective when thestrength of the interference is comparable or greater than that ofbackground noise.

SUMMARY OF THE DISCLOSURE

Accordingly, systems and methods are disclosed for computing softinformation in the presence of interfering signals. These systems andmethods enable wireless communication to occur without having todecompose the frequency spectrum into different frequency transmissionchannels.

The embodiments of the present invention can be employed in any suitablewireless communications system, such as a cellular system (e.g., amobile network) or a wireless Internet system (e.g., a WiMAX network).Using a cellular system as an example, the cellular system may include aplurality of base stations that can each communicate with mobilestations (e.g., cellular telephones) that are within an area assigned tothat base station. When a mobile station is connected to the cellularnetwork, however, the mobile station may receive radio signals from notonly an intended source (e.g., the base station assigned to cover thearea that the mobile station is located in), but from one or moreinterfering sources (e.g., neighboring base stations transmitting datato other mobile stations). Thus, the mobile station may be configured todecode a received signal in a manner that takes into account not onlycharacteristics of the intended source, but also any interferingsources.

Each mobile station can include a plurality of receive antennas fromwhich the mobile station can receive a signal vector. From each antenna,the mobile station can obtain signals from one or more of the intendedand interfering sources through the intended and interference channels.To accurately decode for the intended signal in the presence ofinterfering signals, the mobile station can estimate channel informationfor each interference channel and identify modulation information foreach interfering source. To perform the former, the mobile station cananalyze a pilot signal received from an interfering source, and can usethis analysis to determine a channel gain associated with eachinterference channel. To perform the latter, the mobile station candecode control information, such as a DL-MAP message, that is receivedfrom each interfering source. This control information may be used toidentify the modulation scheme, etc., implemented by the interferingbase stations.

Using the channel information and modulation information, the mobilestation can compute soft information for the information transmitted bythe intended source. The soft information may be in the form of alog-likelihood ratio (LLR), for example. In one embodiment, to computethe soft information, the mobile station can calculate Euclideandistance-based metrics. Calculating a metric may involve computing theEuclidean distance between a receive signal vector and an expectedreceive signal vector for a particular set of intended and interferencesymbols. The expected receive signal vector may be obtained based on thechannel information and the modulation information for not only theintended source/intended channel, but also each interferingsource/interfering channel.

In some embodiments, the mobile station may adaptively determine whichreceive dimensions (e.g., antennas) to use when computing softinformation for the intended information. For example, the mobilestation may use only the signals from a subset of the receive antennasbased on which antennas have the most information on the intended signaland/or least interference from the interfering sources. The mobilestation may use a variant of a sorted QR operation on the channel matrixformed from the intended and interfering channels to identify thereceive dimensions with the highest signal-to-interference plus noiseratio (SINR).

In some embodiments, the mobile station may also adaptively determinewhich transmit dimensions to use when computing soft information. Thatis, the mobile station can determine which of the interfering sources toconsider when computing the soft information. For the interfering basestations that do not strongly affect the receive signal vector, themobile station can ignore their affect. Alternately, the mobile stationcan model these weaker interfering signals as having a Gaussiandistribution. By adaptively determining which receive and/or transmitdimensions to use in computing soft information, an appropriate subsetof receive and/or transmit dimensions can be identified. The subset canbe selected such that the decoding performance of the mobile stationreceiver is not significantly affected, yet allows the mobile station todecode a receive signal vector more efficiently (e.g., using fewerresources, such as speed-based, area-based, or power-based resources).

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects and advantages of the invention will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 is a schematic diagram of an illustrative cellular system;

FIG. 2 is a simplified block diagram of an illustrative base stationtransmitter;

FIG. 3 is a simplified block diagram of an illustrative mobile stationreceiver with a plurality of receive antennas;

FIG. 4 is a 4QAM/QPSK signal constellation set that may be used by thebase station transmitter of FIG. 2 and/or the mobile station receiver ofFIG. 3;

FIG. 5 is a 16QAM signal constellation set that may be used by the basestation transmitter of FIG. 2 and/or the mobile station receiver of FIG.3;

FIG. 6 is a simplified flow diagram of an illustrative process forreducing the number of transmit and/or receive dimensions used forcomputing soft bit metrics;

FIG. 7 is a more detailed, yet still simplified, flow diagram of anillustrative process for reducing the number of receive dimensions usedfor computing soft bit metrics;

FIG. 8 is a more detailed, yet still simplified, flow diagram of anillustrative process for reducing the number of transmit and receivedimensions used for computing soft bit metrics;

FIG. 9 is a block diagram of an exemplary cell phone that can employ thedisclosed technology; and

FIG. 10 is a block diagram of an exemplary media player that can employthe disclosed technology.

DETAILED DESCRIPTION OF THE DISCLOSURE

FIG. 1 shows a simplified diagram of illustrative cellular system 100.Cellular system 100 can include a plurality of base stations that areinterconnected to form a mobile or cellular network. These base stationscan include base stations 122, 142, and 162. Each of these base stationscan be configured to communicate with mobile stations located within aparticular physical area within that base station's radio communicationsrange. The physical area may be referred to as a radio cell. Inparticular, base station 122 may communicate with mobile stations withinradio cell 120, base station 142 may communicate with mobile stationswithin radio cell 140 (e.g., mobile stations 144 and 146), and basestation 162 may communicate with mobile stations within radio cell 160.In FIG. 1, radio cells 120, 140, and 160 are represented by hexagonalregions, although this shape is merely illustrative.

Mobile stations 144 and 146 may be any suitable type of cellulartelephone. Mobile stations 144 and 146 may each have a plurality ofreceive antennas for receiving signals from the base stations of themobile network. For simplicity, the number of receive antennas includedin a mobile station (e.g., mobile station 144 or mobile station 146) maybe referred to by the variable, N_(r), where N_(r)≧1. Mobile stations144 and 146 may operate using any suitable protocol that is compatiblewith base stations 122, 142, and 162, and with the mobile network ingeneral. The base stations and mobile stations of cellular system 100can operate using any suitable conventional cellular protocol, such asthe Global Systems for Mobile communications (“GSM”) standard or thecode division multiple access (“CDMA”) standard, or using anon-conventional protocol.

The base stations and mobile stations in cellular system 100 may use anyof a variety of modulation and coding schemes to enable reliablecommunication. For example, base stations 122, 142, and 162 may operatewith a modulation scheme based on orthogonal frequency divisionmultiplexing (“OFDM”). Further examples of suitable modulation andcoding schemes will be discussed in detail below in connection withFIGS. 2-5. To notify the mobile stations of the modulation and codingused by a base station, base stations 122, 142, and 162 may broadcast acontrol sequence to at least the mobile stations within their respectiveradio cells. In addition to coding and modulation information, thecontrol sequence may also include any other suitable control informationthat the mobile stations may use to interpret the data sent by a basestation. For example, the control sequence may include information onhow the data frames are structured, how many symbols are included ineach frame, and the intended recipient (e.g., mobile station) of thenext data block.

Base stations 122, 142, and 162 may also transmit a pilot signal to eachmobile station within its radio cell to provide each mobile stationwith, among other things, phase alignment information. The pilot signalmay be modulated by a particular pseudo-noise (“PN”) sequence, and eachbase station may utilize a different PN sequence. The different PNsequences may allow the mobile stations (e.g., mobile stations 144 and146) to identify the base station associated with a received pilotsignal. Therefore, mobile stations 144 and 146 may be able to identifyand align their signal transmissions and receptions based on the pilotsignal from base station 142.

Base stations 122, 142, and 162 may broadcast a pilot signal, controlinformation, and network data to all mobile stations that are withinradio communication range. This allows each base station to not onlytransmit information to any mobile station within that base station'sradio cell, but also to mobile stations in neighboring radio cells thatare sufficiently close to the base station. For example, due to theproximity of mobile station 144 to base station 142 in radio cell 140,mobile station 144 may predominantly receive information from basestation 142. Mobile station 146, on the other hand, may be able toreceive information not only from base station 142 in radio cell 140(through channel 150), but may also receive interfering information frombase station 162 in neighboring radio cell 160 (through channel 170) andfrom base station 122 in neighboring radio cell 120 (through channel130). If base stations 122, 142, and 162 operate using the samefrequency band or frequency channel such that signals received fromthese three sources are not easily distinguishable, mobile station 146may suffer from an effect referred to sometimes as “inter-cellco-channel interference” (or simply “co-channel interference” or“interference”). The variable, J, may sometimes be used to define thenumber of base stations (including the intended base stations) that areconsidered to be affecting the signal vector received by mobile station146. Thus, in the scenario illustrated in FIG. 1, J=3.

For simplicity, the radio signal expected by mobile station 146 (e.g.,from base station 142, or the “intended source”) may sometimes bereferred to as the “intended signal,” and channel 150 associated withthe intended source may be referred to as the “intended channel.” Theradio signals received from neighboring base stations (e.g., from basestations 122 or 162, or an “interfering source”) may sometimes bereferred to as interference signals, and channels 130 and 170 associatedwith the interfering sources may be referred to as “interferingchannels.”

Intended channel 150 and interference channels 130 and 170 may each beassociated with a particular channel vector that defines the amount thata signal traveling through the channel gets amplified/diminished and/orphase shifted before reaching the receive antennas of mobile station146. The channel vector for intended channel 150, interference channel130, and interference channel 170 may sometimes be referred to by thevariables, h_(k,1), h_(k,2), and h_(k,2), respectively. Each h_(k,j) maybe an N_(r)-dimensional vector, where the first vector component definesthe channel gain associated with the first receive antenna of mobilestation 146 and the N_(r) ^(th) vector component defines the channelgain associated with the N_(r) ^(th) receive antenna. In these vectors,the k subscript may represent a particular time, frequency, or spatialsample of an intended or interference signal.

Thus, in the example of FIG. 1, mobile station 146 may receive a signalvector, y_(k), that is given mathematically byy _(k) =h _(k,1) x _(k,1) +h _(k,2) x _(k,2) +h _(k,3) x _(k,3) +z_(k).  (EQ. 1)Here, x_(k,j) may be the signal transmitted from the jth base stationfor 1≦j≦J. x_(k,1) may be a signal intended for mobile station 146,while x_(k,2) and x_(k,3) may be interfering signals that are intendedfor mobile stations in radio cells 120 and 160, respectively. z_(k) maybe a vector of the N_(r) additive noise components that affect the N_(r)receive antennas of mobile station 146. In some embodiments, z_(k) maybe background noise that can be modeled as additive white Gaussian noise(AWGN) with a probability distribution function given by

$\begin{matrix}{{AWGN}_{{PDF}❘y_{k}} = {\frac{1}{\sigma\sqrt{2\;\pi}}{{\exp\left( {- \frac{{{y_{k} - {E\left\lbrack y_{k} \right\rbrack}}}^{2}}{2\;\sigma^{2}}} \right)}.}}} & \left( {{EQ}.\mspace{14mu} 2} \right)\end{matrix}$

In general, for J base stations, where the first base station is theintended source, a receive signal vector may be given by

$\begin{matrix}{y_{k} = {{h_{k,1}x_{k,1}} + {\sum\limits_{j = 2}^{J}{h_{k,j}x_{k,j}}} + {z_{k}.}}} & \left( {{EQ}.\mspace{14mu} 3} \right)\end{matrix}$For simplicity, the second term of EQ. 3 may be referred to as theinterference term, and EQ. 3 may be rewritten asy _(k) =h _(k,1) x _(k,1) +w _(k) +z _(k),  (EQ. 4)where w_(k) is a signal component representing the interference term.EQ. 4 may again be written as EQ. 5 below, where EQ. 5 is expressed interms of the intended signal and an interference plus noise component:y _(k) =h _(k,1) X _(k,1) +v _(k)  (EQ. 5)Thus, v_(k) may hereinafter represent the interference plus noisecomponent of a receive signal vector.

The receive signal vector may be more succinctly represented by achannel matrix, a transmit signal vector, and a noise vector. Inparticular, the receive signal vector may be given byy _(k) =H _(k) x _(k) +z _(k),  (EQ. 6)where the channel matrix, H_(k), is a vector concatenation of theindividual channel vectors for each intended and interference channels,e.g.,H _(k) =[h _(k,1) h _(k,2) . . . h _(k,J)].  (EQ. 7)Similarly, the transmit signal vector is a vector composed of eachtransmit signal, e.g.,x _(k) =[x _(k,1) x _(k,2) . . . x _(k,J)]^(T).  (EQ. 8)As shown in EQ. 7 and EQ. 8, the intended channel may be the firstcolumn of the channel matrix and the intended signal may be the firstvector component of the transmit signal vector.

Returning to FIG. 1, in many scenarios, the co-channel interference(e.g., the effect of base stations 122 and 162 on mobile station 146)may be stronger than any noise that occurs during data transmission frombase station to mobile station. This may be especially true when amobile station is near the boundary of two or more radio cells. Incurrent communications protocols, such as GSM, co-channel interferenceis circumvented by having neighboring base stations broadcast networkdata using different frequency channels. For example, if cellular system100 were to operate using one of these current protocols, the mobilenetwork can assign a first frequency channel to base station 122 andradio cell 120, a second frequency channel to base station 142 and radiocell 140, and a third frequency channel to base station 162 and radiocell 160. By having neighboring base stations use different frequencychannels, a mobile station in a particular radio cell can suffer fromlittle to no interference from a base station in a neighboring radiocell. For example, in this scenario, even though mobile station 146 canreceive an interference signal from neighboring base station 162, mobilestation 146 can tune into only the frequency channel of base station 142to ensure that radio signals from base station 162 are substantiallyexcluded.

The communications technique of assigning neighboring base stations orsectors different frequency bands may be referred to as frequency reuse.Cellular system 100 may, as described above, use three differentfrequency channels to implement frequency reuse. Such a communicationssystem may be referred to as having a frequency reuse of 3 or 1/3. GSMillustrates one protocol that can, in some embodiments, be implementedby the mobile network of cellular system 100. GSM uses seven differentfrequency channels and therefore has a frequency reuse of 7 or 1/7.

While frequency reuse ensures that mobile stations will not suffer frommuch interference, frequency reuse does not efficiently utilize thebandwidth made available to cellular systems. That is, cellular systemsare assigned a limited amount of bandwidth. With each base station usingonly a fraction of the available bandwidth, each base station has aspectral efficiency (and therefore a maximum data rate) that is wellbelow the spectral efficiency and data rate that can be achieved.Accordingly, embodiments of the present invention include techniquesthat enable a frequency reuse of one. In particular, embodiments of thepresent invention advantageously provide techniques that can counter theeffects of inter-cell co-channel interference such that using differentfrequency channels in neighboring radio cells or cell sectors isunnecessary.

Thus, in some embodiments, base stations 122, 142, and 162 may transmitdata to mobile stations using up to the full frequency band available tothe mobile network. To ensure reliability in communicating the controlmessage, which in turn allows a mobile station to accurately interpretdata, the control message may be transmitted with strong encoding andwith frequency reuse. For the example of FIG. 1, base stations 122, 142,and 162 may operate using a frequency reuse of 3 or 1/3 whentransmitting control information and may operate using a frequency reuseof one when transmitting data. This example applies to WiMax systems,which, if implemented here, may transmit control information referred toas a DL-MAP message with a frequency reuse of 3 or 1/3. A transmissionscheme that uses frequency reuse only when transmitting controlinformation may be advantageous, as reliability in communicating thecontrol message is maintained without concern for inter-cell co-channelinterference, while data (which can constitute the majority of theinformation transmitted from a base station) is transmitted with highspectral efficiency and data rate.

While some embodiments of the present invention are described in termsof a mobile station that receives intended and interfering informationfrom various base stations, this is merely to simplify the descriptionof the present invention. These embodiments may also be used to allow abase station to handle intended and interfering information receivedfrom various mobile stations. That is, some or all of the embodimentsdescribed herein for the downlink scenario may also be applied to theuplink scenario. Also, the present invention may be implemented not onlyin cellular systems, but in any application that may suffer frominter-cell co-channel interference.

FIG. 2 shows a simplified block diagram of base station transmitter 200that can prepare network information 210 for transmission as radiosignal 270. In some embodiments, base station transmitter 200 may beimplemented as the transmitter for one or more of base stations 122,142, and 162 of FIG. 1. Base station transmitter 200 can include encoder220, bit-interleaver 240, and Gray mapper/modulator 260.

Encoder 220 may encode network information 210 based on a suitable errorcorrecting code (“ECC”). For example, encoder 220 may operate using aconvolutional code (e.g., a rate-1/2, rate-2/3 convolutional code,convolutional Turbo code) of memory m. Encoder 220 may therefore convertnetwork information 210, which may be some form of digital information(e.g., a stream of binary data), into an encoded stream of binary data.Since encoder 220 may have a memory of m, each m consecutive bits in theencoded stream created by encoder 220 depends on the value of the sameone bit of network information 210. In order to remove any adverseeffects that may result from this dependency (e.g., the inability toreliably decode when burst errors are present), the encoded stream maybe interleaved by bit-interleaver 240. In particular, bit-interleaver240 may change the order of the bits in the encoded stream to ensurethat neighboring bits in the interleaved sequence are effectivelyindependent of each other.

Gray mapper/modulator 260 of base station transmitter 200 may beconfigured to convert the interleaved digital sequence produced bybit-interleaver 240 into a signal for transmission. Graymapper/modulator 260 may first group bits of the interleaved sequenceinto symbols based on the size of a modulation scheme, and may thenmodulate the symbols into a signal having a particular magnitude andphase specified by the modulation scheme. Gray mapper/modulator 260 mayuse any suitable modulation scheme of any of a variety of sizes. Forexample, Gray mapper/modulator 260 may utilize a quadrature amplitudemodulation (“QAM”) scheme (e.g., 4QAM, 16QAM, 32QAM) or a phase shiftkeying (“PSK”) modulation scheme (e.g., QPSK, 16PSK, 32PSK).

The particular modulation scheme employed by Gray mapper/modulator 260may be designed to operate effectively with the particular errorcorrecting code (ECC) employed by encoder 220. This type ofcommunications technique is commonly referred to as coded modulation.Therefore, as base station transmitter 200 of FIG. 2 also includesbit-interleaver 240, the overall communications technique employed bybase station transmitter 200 can be referred to as bit-interleaved codedmodulation (“BICM”).

The modulation scheme used by Gray mapper/modulator 260 may beassociated with a signal constellation set that defines the magnitudeand phase of a carrier signal that is transmitted for each possiblesymbol value. For example, FIG. 4 shows an illustrative signalconstellation set 400 for a 4QAM/QPSK modulation system, and FIG. 5shows an illustrative signal constellation set 500 for a 16QAMmodulation scheme. In these figures, the respective constellation setsare shown on a complex number plane, where each “+” represents a signalconstellation point having a particular phase and magnitude. Forexample, referring to FIG. 4, signal constellation point 410 has amagnitude of one and a phase of +45 degrees. Thus, when that signalconstellation point is selected for transmission, Gray mapper/modulator260 may produce a radio signal that has a magnitude of one and a phaseof +45 degrees.

Each signal constellation point in signal constellation sets 400 isassociated with a particular two-bit symbol, and each signalconstellation point in set 500 is associated with a particular 4-bitsymbol. The symbols in these respective constellation sets may beassigned to particular signal constellation points based on a Gray codemapping. A Gray code mapping maps neighboring signal points in themodulation scheme to symbols that differ in only one bit. For example,in FIG. 4, the two signal points that correspond to symbols differing bytwo bits (“00” and “11”) are not neighboring signal points. Gray codemapping therefore ensures that, even if a signal were mistaken for aneighboring signal point when decoded, the incorrectly decoded signalcan be incorrect in only one bit.

Returning to FIG. 2, Gray code mapper/modulator 260 may produce radiosignal 270, or x_(k), for transmission to one or more mobile stations(e.g., mobile stations 144 or 146). If different symbols are transmittedin different time periods (e.g., symbol periods), x_(k) may representthe value of radio signal 270 sampled at time k. In some embodiments, krepresents another type of dimension of radio signal 270 other thantime, such as a spatial dimension or frequency dimension. Radio signal270 may be transmitted through a frequency channel (e.g., channel 170 ofFIG. 1) and received by the receive antennas of a mobile stationreceiver, such as the mobile station receiver shown in FIG. 3.

FIG. 3 shows a simplified block diagram of mobile station receiver 300.In some embodiments, mobile station receiver 300 may be implemented aspart of one or both of mobile stations 144 and 146. Mobile stationreceiver 300 can include N_(r) receive antennas that are each operableto receive a signal that is a superposition of the intended signal(e.g., radio signal 270) and one or more interference signals. That is,mobile station receiver 300 may receive signal vector 370 from thesereceive antennas that can be mathematically represented by EQ. 3 throughEQ. 6 above. Thus, each vector component of receive signal vector 370may be a linear combination of the signals transmitted from the J (e.g.,three) base stations.

Mobile station receiver 300 can be configured to decode receive signalvector 370 and obtain an estimate of the originally transmittedinformation (e.g., network information 210 of FIG. 2). To decode receivesignal vector 370, mobile station receiver 300 can include softbit-metric calculator 360, de-interleaver 340, and decoder 320. Each ofthese receiver components may correspond to a transmitter component inbase station transmitter 200 and may effectively undo the operationperformed by the corresponding transmitter component. For example, softbit-metric calculator 360 may correspond to Gray mapper/modulator 260that can demodulate/de-map receive signal vector 370 using at least thesame modulation scheme and signal constellation set as that used by Graymapper/modulator 260. De-interleaver 340 may correspond tobit-interleaver 240 and may return the symbol order of the received datainto its original order, e.g., the order expected by decoder 320.Decoder 320 may be a soft-decoder that corresponds to encoder 220, andmay perform decoding based on the same error correcting code (e.g.,convolutional code) as encoder 220. Thus, decoder 320 may produceestimate 310 of network information (e.g., network information 210). Insome embodiments, decoder 320 may be a Viterbi decoder or a Turbodecoder. If mobile station receiver 300 successfully interprets receivesignal vector 360, estimate 310 may be the same digital sequence asnetwork information 210.

Referring to soft bit-metric calculator 360 of FIG. 3 in more detail,soft bit-metric calculator 360 may calculate soft information for eachbit of information contained within the intended signal. The softinformation may be in the form of a log-likelihood ratio (“LLR”) foreach received bit of intended information. Alternatively, the softinformation can be proportional to an LLR. Soft bit-metric calculator360 may calculate an LLR according to EQ. 8:

$\begin{matrix}{{{LLR}\left( {b_{i}❘y_{k}} \right)} = {\log\frac{\Pr\left( {b_{i} = {0❘y_{k}}} \right)}{\Pr\left( {b_{i} = {1❘y_{k}}} \right)}}} & \left( {{EQ}.\mspace{14mu} 8} \right)\end{matrix}$where b_(i) is the transmitted bit of the intended information containedwithin y_(k) for which the LLR is being calculated. Soft bit-metriccalculator 360 can obtain a reliable log-likelihood ratio based on EQ. 8by using accurate estimates of the channel information for the intendedchannel and any interference channels, as well as modulation informationfor these channels.

To compute the channel information estimate, mobile station receiver 300may, for example, include computational logic (not shown) that isconfigured to estimate the interference channel gain for each receiveantenna and each interfering source. The computational logic may also beconfigured to compute the intended channel gain for each receiveantenna. The computational logic can compute these channel informationestimates by analyzing the characteristics of pilot signals receivedfrom each base station. Because each signal source modulates the pilotsignal based on a unique PN sequence, the computational logic candistinguish between the different pilot signals. From the analysis ofvarious pilot signals, the computational logic produces an estimate ofthe interference and/or intended channel gain, for example. Mobilestation receiver 300 may compute the channel information estimates atany suitable time during operation, such as at power-up, when initiallyconnected to a base station, periodically, or whenever the pilot signalis transmitted, etc. Channel information estimates can be computed inthis manner for embodiments where receiver 300 is implemented on amobile station and for embodiments where receiver 300 is implemented ona base station.

Mobile station receiver 300 may also include a control informationdecoder (not shown) to compute the modulation information for anintended source and any interference sources. For example, in a WiMAXsystem, receiver 300 may include a DL-MAP decoder that decodes a DL-MAPmessage received from each base station. From the DL-MAP message, mobilestation receiver 300 can retrieve the modulation information. Asdescribed above, the modulation information may include the modulationscheme (e.g., QAM, PSK, PAM), the size of the modulation scheme, and themagnitude/phase associated with the modulation scheme. Since a DL-MAPmessage or other control message may be heavily encoded and may betransmitted using frequency reuse, the receiver may be able toaccurately decode the control information from the interfering sourceeven if noise and/or interference prevents receiver 300 from accuratelydecoding regular data from the interfering source.

Using the estimated channel information and the modulation informationfor the intended and interference sources, soft bit-metric calculator360 of mobile station receiver 300 (FIG. 3) can accurately determine theexpected receive signal vector for a given transmit signal vector in thepresence of AWGN, which can be useful as a comparison to the actualreceive signal vector. In particular, rather than computing the expectedreceive signal vector based on only the intended signal, soft bit-metriccalculator 360 may compute the expected receive signal vector based onsubstantially all of the signals that can affect the receive signalvector. For example, referring briefly again to FIG. 4, if aninterfering source operates with a 4QAM scheme and signal constellationset 400, soft bit-metric calculator 360 can compute the expected receivesignal vector with the knowledge that the interference from thisinterfering source takes on only four possible values (e.g., magnitudeof one with four different phases). Thus, this technique improves uponconventional systems, such as matched filter-based or equalization-based(e.g., ZF or MMSE-based) systems, which operate under the assumptionthat all interfering signals may be modeled as AWGN with zero mean.

With continued reference to FIG. 3, soft bit-metric calculator 360 mayoperate under the assumption that only the background noise, z_(k), canbe modeled as AWGN, and can be configured to compute the log-likelihoodratio for bit b_(i) of the intended information according to

$\begin{matrix}{{LLR}_{i} = {{\log\left( {\sum\limits_{{x_{1} \in X_{1,l_{i}}^{(1)}},\ldots\mspace{14mu},{x_{j} \in X_{J}}}{\exp\left( {- \frac{{{y_{k_{i}} - {\sum\limits_{j = 1}^{J}{h_{k_{i},j}x_{j}}}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x_{1} \in X_{1,l_{i}}^{(0)}},\ldots\mspace{14mu},{x_{j} \in X_{J}}}{\exp\left( {- \frac{{{y_{k_{i}} - {\sum\limits_{j = 1}^{J}{h_{k_{i},j}x_{j}}}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)}}} & \left( {{EQ}.\mspace{14mu} 9} \right)\end{matrix}$In EQ. 9, X_(i) ^((j)) is the set of symbols that have a bit value of jat bit position b_(i) and σ_(z) ² is the power of the noise, z_(k).Here, the expression

${\sum\limits_{j = 1}^{J}{h_{k_{i},j}x_{j}}} = {H_{k}x_{k}}$in both logarithm computations produces an accurate estimate of theexpected receive signal vector for each possible transmit vector (e.g.,in FIG. 4, the four different possible values for each signal component,and in FIG. 5, the 16 different possible values for each signalcomponent). This expression is a function of the channel vector for eachbase station and a transmit signal from each base station. Therefore,the expression uses both the channel information estimate (to obtaineach h_(k) _(i) _(,j)) and the modulation information (e.g., informationon a signal constellation set, such as those in FIGS. 4 and 5, to obtaineach x_(j)).

It should be understood that EQ. 9, and any of the other LLR equationsprovided below are merely illustrative, and that other LLR equations maybe computed by soft bit-metric calculator 360 without departing from thescope of the present invention. For example, soft bit-metric calculator360 can operate using a different equation that is based on a distancebetween a receive signal vector and one or more expected receive signalvectors. This distance calculation may sometimes be referred to as a“distance metric.” Soft bit-metric calculator 360 can operate using anysuitable distance metric, including but not limited to the distancemetric of EQ. 9.

Instead of computing EQ. 9, in some embodiments, an approximation can beimplemented to simplify the complexity of the hardware (e.g., logic) orsoftware. For example, of soft bit-metric calculator 360 may employ anapproximation for computing logarithms (referred to sometimes as themax-log-map approximation), and can instead calculate,

$\begin{matrix}{{LLR}_{i,{approx}} = {{\frac{1}{\sigma_{z}^{2}}\left\lbrack {{\min\limits_{x_{1} \in X_{1,l_{i},\ldots,{x_{j} \in X_{J}}}^{(1)}}\left\{ {{y_{k_{i}} - {\sum\limits_{j = 1}^{J}{h_{k_{i},j}x_{j}}}}}^{2} \right\}} - {\min\limits_{x_{1} \in X_{1,l_{i},\ldots,{x_{j} \in X_{J}}}^{(0)}}\left\{ {{y_{k_{i}} - {\sum\limits_{j = 1}^{J}{h_{k_{i},j}x_{j}}}}}^{2} \right\}}} \right\rbrack}.}} & \left( {{EQ}.\mspace{14mu} 10} \right)\end{matrix}$Note that EQ. 10, unlike EQ. 9, advantageously does not includepotentially resource-intensive exponential or logarithm computations.Moreover, EQ. 10 ultimately uses only two possible values of thetransmit signal vector (a first value with b_(i)=0 and a second valuewith b_(i)=1) to compute the approximate LLR, and not all of thepossible values of the transmit signal vector.

The computations of EQ. 9 and EQ. 10 can be further simplified byaltering the squared Euclidean distance calculation. That is, ratherthan computing the distance metric (“DM”),DM=∥y _(k) −H _(k) x _(k)∥²  (EQ. 11)in EQ. 9 and/or EQ. 10, the squared distance computation may be alteredin a way that does not change the result of the computation. Forexample, soft bit-metric calculator 360 may compute the distance metric,DM=∥U _(k) *y _(k) −U _(k) *H _(k) x _(k)∥²  (EQ. 12)in place of the squared Euclidean distances shown in EQ. 9 and/or EQ.10. Here, U_(k)* is the inverse of a unitary matrix, U_(k). Because themultiplication is performed using a unitary matrix, this multiplicationdoes not affect the magnitude of each component, and therefore also doesnot affect the squared Euclidean distance.

In some embodiments, the unitary matrix may be Q_(k), which is theunitary matrix that results from the QR decomposition of the channelmatrix. In particular, the channel matrix may be decomposed as follows:H _(k) =Q _(k) R _(k),  (EQ. 13)where Q_(k) is an N_(r)×N_(r) unitary matrix and R_(k) is an N_(r)×Jupper triangular matrix. By preprocessing the channel matrix usingQ_(k)* (e.g., using a preprocessor implemented on mobile stationreceiver 300 that is not shown), the Euclidean distance metric may bereduced toDM=∥Q _(k) *y _(k) −R _(k) x _(k)∥²  (EQ. 14)for the different possible transmit signal vectors. The critical path inthe computation of the Euclidean distance metric is the multiplicationof a matrix by the transmit signal vector, since this multiplication canoccur for up to every possible value of the transmit signal vector.Therefore, by altering the Euclidean distance metric such that thetransmit signal vector is multiplied by an upper triangular matrixinstead of a full matrix, the computational complexity of the Euclideandistance metric may be decreased substantially. It should be understoodthat the use of Q_(k) as the unitary matrix is merely illustrative, andother suitable unitary matrices may be selected instead.

In some operating scenarios, the strength of interfering signals may bestrong compared to that of the intended signal on only a subset of thereceive antennas. That is, the signal-to-interference plus noise ratio(SINR) may be different for different receive antennas such that some ofthe antennas are strongly affected by interfering signals while othersare not. A mobile station can be configured to compute soft informationusing only those receive antennas having a SINR with at least apredetermined value (e.g., 2 dB, 5 dB, etc.). For example, referringback to FIG. 1, mobile station 146 may have three receive antennas,where the first receive antenna receives primarily the signal fromintended base station 142 (high SINR), the second receive antennareceives primary the signal from interference base station 122 (lowSINR), and the third receive antenna receives primary the signal frominterference base station 162 (low SINR). In this example, mobilestation 146 may be able to accurately estimate the intended informationusing only the signal received on the first antenna. The signalsreceives on ⅔ of the receive antennas may be ignored, thereby decreasingthe amount and/or complexity of the computations necessary to obtainsoft information for the intended information. More particularly, mobilestation 146 may compute EQ. 9 or EQ. 10 to obtain soft information, butcan compute these equations using fewer than the N_(r) components of thereceive and channel vectors. As described in greater detail below, themobile stations configured in accordance with the principles of thepresent invention, such as mobile station 146, may adaptively determinea subset of the receive dimensions (e.g., receive antennas) to use whendecoding a receive signal vector.

In some operating scenarios, an interfering source may not have a strongeffect on a mobile station compared to the intended source and/or otherinterfering sources. For example, referring again to FIG. 1, as mobilestation 146 travels towards the right of the figure and away from radiocell 120, the interference that mobile station 146 experiences may nolonger be strongly affected by the signals transmitted by base station122. Therefore, mobile station 146 may completely stop including theinterfering signal from base station 122 in the log-likelihood equationof EQ. 9 and/or EQ. 10. In particular, when computing EQ. 9 and/or EQ.10, mobile station 146 may compute fewer total Euclidean distancemetrics, because of the decrease in number of total possible transmitsignal vectors. Also, the number of computations in each Euclideandistance metric may be decreased, since J is decreased. Therefore, byadaptively decreasing the number of base stations considered to be aspecific subset of base stations, the complexity of computing softinformation can be minimized.

In some embodiments, rather than completely ignoring base stations whenthey are adaptively determined to not have a strong effect, theinterference signals from these base stations may instead be modeled asbackground noise. For example, mobile station 146 of FIG. 1 can modelthe interference signal as AWGN, and can compute EQ. 9 and/or EQ. 10 byincluding the power of the interfering signal with σ_(z) ², the power ofthe background noise. This way, the complexity of computing softinformation can be decreased without completely ignoring the presence ofan interfering source.

Referring again to FIG. 3, mobile station receiver 300 may use a subsetof the receive dimensions (e.g., receive antennas) when computing alog-likelihood ratio by applying a matrix having orthogonal columns toEQ. 9 and/or EQ. 10 above. This matrix may sometimes be referred to as amodifying matrix and may be denoted by Ũ_(k). The modifying matrix maybe an N_(r)×M matrix, where M is the number of receive dimensionsselected for inclusion in the subset, and therefore M≦N_(r).

Soft bit-metric calculator 360 can compute an exact value of alog-likelihood ratio using distance metrics based on the originalchannel matrix (EQ. 11) or the channel matrix modified by a unitarymatrix (FIG. 12). Using the modifying matrix, soft bit-metric calculator360 can instead compute an approximate value of the LLR. Soft bit-metriccalculator 360 may modify both the received signal vector and theoriginal channel matrix by multiplying each by the inverse of themodifying matrix, e.g.,{tilde over (y)} _(k) =Ũ _(k) *y _(k), and  (EQ. 15){tilde over (H)} _(k) =Ũ _(k) *H _(k).  (EQ. 16)The product of EQ. 15 may sometimes be referred to as a modified receivesignal vector, and the product of EQ. 16 may sometimes be referred to asa modified channel matrix. Soft bit-metric calculator 360 may then usean approximate distance metric given byDM≈DM_(approx,RX) =∥{tilde over (y)} _(k) −x _(k)∥²  (EQ. 17)to calculate the log-likelihood ratio or another suitable soft bitmetric. For example, in some embodiments, soft bit-metric calculator 360can compute

$\begin{matrix}{{{LLR}_{i} = {{\log\left( {\sum\limits_{{x_{1} \in X_{1,l_{i}}^{(1)}},\ldots,{x_{j} \in X_{j}}}{\exp\left( {- \frac{{{{\overset{\sim}{y}}_{k_{1}} - {\sum\limits_{j = 1}^{J}{{\overset{\sim}{h}}_{k_{i},j}x_{j}}}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{x_{1} \in X_{1,l_{i},\ldots,{x_{j} \in X_{J}}}}{\exp\left( {- \frac{{{{\overset{\sim}{y}}_{k_{i}} - {\sum\limits_{j = 1}^{J}{{\overset{\sim}{h}}_{k_{i},j}x_{j}}}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)}}},} & \left( {{EQ}.\mspace{14mu} 18} \right)\end{matrix}$which has the same form as EQ. 9 except that EQ. 18 includes themodified receive signal vector and the modified channel matrix insteadof the original receive signal vector and original channel matrix. Insome embodiments, soft bit-metric calculator 360 may use the max-log-mapapproximation, and can compute the log-likelihood ratio for eachtransmitted bit according to,

$\begin{matrix}{{LLR}_{i,{approx}} = {{\frac{1}{\sigma_{z}^{2}}\left\lbrack {{\min\limits_{x_{1} \in X_{1,l_{i},\ldots,{x_{j} \in X_{J}}}^{(1)}}\left\{ {{{\overset{\sim}{y}}_{k_{i}} - {\sum\limits_{j = 1}^{J}{{\overset{\sim}{h}}_{k_{i},j}x_{j}}}}}^{2} \right\}} - {\min\limits_{x_{1} \in X_{1,l_{i},\ldots,{x_{j} \in X_{J}}}^{(0)}}\left\{ {{{\overset{\sim}{y}}_{k_{i}} - {\sum\limits_{j = 1}^{J}{{\overset{\sim}{h}}_{k_{i},j}x_{j}}}}}^{2} \right\}}} \right\rbrack}.}} & \left( {{EQ}.\mspace{14mu} 19} \right)\end{matrix}$

For example, with three receive antennas and two interfering sources,soft bit-metric calculator 360 may use a matrix with two columns, suchas

$\begin{matrix}{{{\overset{\sim}{U}}_{k} = \begin{bmatrix}1 & 0 \\0 & 1 \\0 & 0\end{bmatrix}},} & \left( {{EQ}.\mspace{14mu} 20} \right)\end{matrix}$so that only two receive dimensions are considered when Ũ_(k)* ismultiplied by the receive signal vector in the Euclidean distance-basedmetrics of EQ. 17.

When only one receive antenna is used to compute soft informationaccording, the Euclidean distance-based metric of EQ. 17 reduces to

$\begin{matrix}{{DM} = {{r_{k} - {\sum\limits_{j = 1}^{J}{u_{k}^{*}h_{k,j}x_{k,j}}}}}^{2}} & \left( {{EQ}.\mspace{14mu} 21} \right)\end{matrix}$for a filtered receive signal given by

$\begin{matrix}{r_{k} = {{u_{k}^{*}y_{k,1}} = {{\sum\limits_{j = 1}^{J}{u_{k}^{*}h_{k,j}x_{k,j}}} + {u_{k}^{*}{z_{k}.}}}}} & \left( {{EQ}.\mspace{14mu} 22} \right)\end{matrix}$In this case, the modifying matrix Ũ_(k) may be any column vector, u_(k)with a norm of one. In some embodiments, a normalized matched filterapproach may be used, where the column vector can be

$\begin{matrix}{u_{k} = {\frac{h_{k,1}}{h_{k,1}}.}} & \left( {{EQ}.\mspace{14mu} 23} \right)\end{matrix}$Any other suitable column vector may be used instead of the vector givenby EQ. 23, such as a minimum-mean squared error (MMSE) filter.

In some embodiments, soft bit-metric calculator 360 may reduce not onlythe number of receive dimensions when computing soft information, butmay also reduce the number of transmit dimensions. To compute an LLRusing all J transmit dimensions that are initially considered, softbit-metric calculator 360 uses the approximate distance metric of EQ.17, reproduced as EQ. 24:

$\begin{matrix}{{DM}_{{approx},{Rx}} = {{{{\overset{\sim}{y}}_{k} - {{\overset{\sim}{H}}_{k}x_{k}}}}^{2} = {{{{\overset{\sim}{y}}_{k} - {\sum\limits_{j = 1}^{J}{{\overset{\sim}{h}}_{k,j}x_{j}}}}}^{2}.}}} & \left( {{EQ}.\mspace{14mu} 24} \right)\end{matrix}$In some embodiments, soft bit-metric calculator 360 may need to evaluateEQ. 24 for every possible value of the transmit signal vector, x_(k).This computation may be resource-intensive, especially for larger valuesof J. Therefore, in some embodiments, soft-bit metric calculator 360 mayignore one or more interfering sources, j, when the correspondingmodified channel vector {tilde over (h)}_(k,j) has a low channel gain(e.g., a low norm), or soft-bit metric calculator 360 may treat the oneor more interfering sources as background noise sources (e.g., AWGNsources). Soft bit-metric calculator 360 may then compute softinformation with a distance metric given by,

$\begin{matrix}{{DM}_{{approx},{Rx},{Tx}} = {{{{\overset{\sim}{y}}_{k} - {\sum\limits_{j \in S_{J}}{{\overset{\sim}{h}}_{k,j}x_{j}}}}}^{2}.}} & \left( {{EQ}.\mspace{14mu} 25} \right)\end{matrix}$In EQ. 25, S_(J) is the set of indices corresponding to the intendedsource and any significant interfering sources. The selection of the setS_(J) can determine the decoding performance of the receiver. Dependingon the number of interfering sources and which of the interferingsources are included, the approximation may cause the decodingperformance of the receiver to be relatively accurate or relativelyrough.

In some embodiments, soft bit-metric calculator 360 can reduce thenumber of transmit dimensions used in LLR computation without reducingthe number of receive dimensions used. In these embodiments, themodifying matrix may be a unitary matrix of full dimension.

Referring now to FIGS. 6-8, simplified flow diagrams of illustrativeprocesses are shown for adaptively selecting the receive and/or transmitdimensions to use for computing soft information. These processes may beperformed by any suitable mobile station (e.g., mobile station 144 ormobile station 146 of FIG. 1), and the mobile station may be configuredto execute one or more of these processes at any suitable time. Forexample, the mobile station may execute the steps of one or more of theprocesses automatically, by request of the intended base station,periodically (e.g., once every few seconds or minutes), on power-up ofthe mobile station, or at any other suitable time. It should beunderstood that the steps shown in these flow diagrams are merelyillustrative, and that any of the illustrated steps may be removed,modified, or combined, or any additional steps may be added, withoutdeparting from the scope of the present invention.

Referring first to FIG. 6, a simplified flow diagram of process 600 isshown for selecting receive and transmit dimensions. At step 602, themobile station may identify the strength (e.g., power) of the intendedsignal compared to that of the interfering signals for each receivedimension. For example, the mobile station can analyze the channelmatrix associated with the intended and receive channels, and cancompute the SINR of each receive dimension. Then, at step 604, themobile station can select a subset of the receive dimensions for use indistance-based decoding (e.g., computing LLRs). The mobile station canselect, for example, M receive dimensions that have the highest SINR, orthe mobile station can select only those receive antennas that have aSINR of at least a predetermined value (e.g., 2 dB, 5 dB, etc.). Thismay allow the mobile station to choose receive dimensions that havestrong intended signals and/or weak interfering signals.

At step 606, the mobile station may determine the effect that eachtransmit signal vector can have on the receive signal vector. Forexample, since each column of the channel matrix is associated with aparticular transmit dimension, the mobile station can compute the normof each column of the channel matrix to interpret the overall effecteach transmit dimension may have. Then, at step 608, the mobile stationcan select a subset of the transmit antennas that will be used fordistance-based decoding. The mobile station may select, for example, apredetermined number of transmit dimensions having the highest norm, orthe mobile station can select only those transmit dimensions that have anorm of a predetermined value. At step 608, the mobile station canperform the distance-based decoding for the intended information usingthe subset of receive dimensions previously obtained at step 604 andusing the subset of transmit dimensions previously obtained at step 608.The mobile station may therefore obtain a log-likelihood ratio oranother soft bit metric associated with the received intendedinformation.

Referring now to FIG. 7, a simplified flow diagram of process 700 isshown for adaptively selecting receive dimensions. Process 700 may be amore detailed representation of some of the steps shown in process 600of FIG. 6. At step 702, the mobile station may perform a variant of asorted QR operation on the channel matrix. The original sorted QRoperation is a modification of the Gram-Schmidt process for decomposinga matrix, but at each step, the column with the largest norm in the nullspace spanned by the already selected columns is obtained. The mobilestation performs the sorted QR process, except that the mobile stationselects

$\frac{h_{k,1}}{h_{k,1}}$as the first orthogonal column vector q_(k,1). The variation from theoriginal sorted QR operation allows the mobile station to retain all ofthe energy of the received signal that corresponds to the intendedsource, as represented by the channel vector h_(k,1). At the same time,this operation performed at step 702 may arrange the rows of theresulting R_(k) matrix based on the signal strength of the intendedsignal.

Then, at step 704, the first row of the R_(k) matrix is selected, sincethe first row of this decomposed channel matrix is associated with theintended signal. The remaining rows are associated purely with theinterfering sources. Thus, at step 706, to choose the interferencechannels corresponding to interfering sources with greater power, themobile station selects rows other than the first row that have anon-zero element larger than a predetermined value. For example, themobile station may select those rows with a non-zero element greaterthan

$\frac{h_{k,1}}{f(S)}.$Here, S may be the SINR desired by the mobile station (e.g., 2 dB, 5 dB,etc.), and ƒ can represent any suitable function (e.g., multiplicationby a constant).

Moving to step 708, the mobile station forms a reduced R_(k) matrix,referred to as an R_(k) matrix, and a reduced Q_(k) matrix, referred toas a {tilde over (Q)}_(k) matrix. These reduced matrices may be formedfrom the M rows previously selected at steps 704 and 706. For example,{tilde over (R)}_(k) may be a sub-matrix of R_(k), which includes onlythe selected rows of R_(k). Thus, the dimension of {tilde over (R)}_(k)is M×J. The {tilde over (Q)}_(k) matrix may be formed from includingonly the first M columns of Q_(k), which produces a matrix of dimensionN_(r)×M. This {tilde over (Q)}_(k) matrix can be used as the modifyingmatrix in the distance metric computations of EQ. 15 through EQ. 17above. Thus, at step 710, the mobile station can compute alog-likelihood ratio or other soft bit metric for the intended signalusing the reduced matrices. In some embodiments, the mobile station cancompute EQ. 18 and/or EQ. 19 above such that the squared Euclideandistance metrics are computed according to

$\begin{matrix}{{{DM} = {{{{\overset{\sim}{Q}}_{k}^{*}y_{k}} - {{\overset{\sim}{R}}_{k}\begin{bmatrix}x_{k,o_{1}} \\\vdots \\x_{k,o_{J - 1}} \\x_{k,o_{J}}\end{bmatrix}}}}^{2}},} & \left( {{EQ}.\mspace{14mu} 26} \right)\end{matrix}$where the variables in EQ. 26 are defined above.

Referring now to FIG. 8, a simplified flow diagram of illustrativeprocess 800 is shown for adaptively selecting receive and transmitdimensions for use in decoding for an intended signal. Process 800 maybe a more detailed representation of some or all of the steps shown inprocess 600 of FIG. 6. At step 802, the mobile station calculates thenorm of each column of the channel matrix, and at step 804, the mobilestation chooses the columns of the channel matrix having a norm greaterthan a predetermined value. For example, the mobile station may select Mcolumns that have a norm larger than

$\frac{h_{k,1}}{f(S)},$where S may again be the SINR desired by the mobile station (e.g., 2 dB,5 dB, etc.), and ƒ can again represent any suitable function (e.g.,multiplication by a constant).

At step 806, the mobile station may reorder the columns of the channelmatrix. More particularly, the mobile station can move the columns withnorms larger than the predetermined value to the first M columns of thechannel matrix. Then, at step 808, the mobile station may perform QRdecomposition on the reordered channel matrix. This may produce squarematrix Q_(k) and upper triangular matrix R_(k). From these resultingmatrices, the mobile station can construct reduced matrices orsub-matrices, {tilde over (Q)}_(k) and {tilde over (R)}_(k,sq), at step810. For example, the mobile station can construct the {tilde over(Q)}_(k) matrix by retaining only the first M columns of Q_(k). Themobile station can construct the {tilde over (R)}_(k,sq) matrix byretaining only the first M columns of the R_(k) matrix to produce {tildeover (R)}_(k), and then retaining only M rows of {tilde over (R)}_(k).

Using the sub-matrices constructed at step 808, the mobile station cancompute log-likelihood ratios for the intended information at step 812.For example, the mobile station can compute EQ. 15 and EQ. 16 aboveusing the {tilde over (Q)}_(k) matrix as the Ũ_(k) sub-matrix. Thus, theEuclidean distance-based metric in EQ. 25 becomes:

$\begin{matrix}{{DM} = {{{{\overset{\sim}{Q}}_{k}^{*}y_{k}} - {{\overset{\sim}{R}}_{k,{sq}}\begin{bmatrix}x_{k,o_{1}} \\\vdots \\x_{k,o_{M - 1}} \\x_{k,o_{M}}\end{bmatrix}}}}^{2}} & \left( {{EQ}.\mspace{14mu} 27} \right)\end{matrix}$Here, x_(k,o) ₁ , . . . , x_(k,o) _(M) represent the signal componentsof the transmit signal vector that correspond to the selected columns ofthe channel matrix. That is, these signal components are a subset of theoriginal transmit dimensions that were selected for use in computingsoft information. The mobile station can calculate an LLR using EQ. 18and/or EQ. 19, or may use the distance metric to compute a soft bitmetric in any other suitable form.

Referring now to FIGS. 9 and 10, various exemplary implementations ofthe present invention are shown.

Referring now to FIG. 9, the present invention can be implemented in acellular phone 950 that may include a cellular antenna 951. The presentinvention may implement either or both signal processing and/or controlcircuits, which are generally identified in FIG. 13 at 952, a WLANnetwork interface 968 and/or mass data storage 964 of the cellular phone950. In some implementations, the cellular phone 950 includes amicrophone 956, an audio output 958 such as a speaker and/or audiooutput jack, a display 960 and/or an input device 962 such as a keypad,pointing device, voice actuation and/or other input device. The signalprocessing and/or control circuits 952 and/or other circuits (not shown)in the cellular phone 950 may process data, perform coding and/orencryption, perform calculations, format data and/or perform othercellular phone functions.

The cellular phone 950 may communicate with mass data storage 964 thatstores data in a nonvolatile manner such as optical and/or magneticstorage devices for example hard disk drives and/or DVDs. The HDD may bea mini HDD that includes one or more platters having a diameter that issmaller than approximately 1.8″. The cellular phone 950 may be connectedto memory 966 such as RAM, ROM, nonvolatile memory such as flash memoryand/or other suitable electronic data storage. The cellular phone 950also may support connections with a WLAN via WLAN network interface 968.

Referring now to FIG. 10, the present invention can be implemented in amedia player 1000. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 15 at 1004, WLAN network interface 1016 and/or massdata storage 1010 of the media player 1000. In some implementations, themedia player 1000 includes a display 1007 and/or a user input 1008 suchas a keypad, touchpad and the like. In some implementations, the mediaplayer 1000 may employ a graphical user interface (GUI) that typicallyemploys menus, drop down menus, icons and/or a point-and-click interfacevia the display 1007 and/or user input 1008. The media player 1000further includes an audio output 1009 such as a speaker and/or audiooutput jack. The signal processing and/or control circuits 1004 and/orother circuits (not shown) of the media player 1000 may process data,perform coding and/or encryption, perform calculations, format dataand/or perform any other media player function.

The media player 1000 may communicate with mass data storage 1010 thatstores data such as compressed audio and/or video content in anonvolatile manner. In some implementations, the compressed audio filesinclude files that are compliant with MP3 format or other suitablecompressed audio and/or video formats. The mass data storage may includeoptical and/or magnetic storage devices for example hard disk drives HDDand/or DVDs. The HDD may be a mini HDD that includes one or moreplatters having a diameter that is smaller than approximately 1.8″. Themedia player 1000 may be connected to memory 1014 such as RAM, ROM,nonvolatile memory such as flash memory and/or other suitable electronicdata storage. The media player 1000 also may support connections with aWLAN via WLAN network interface 1016. Still other implementations inaddition to those described above are contemplated.

The foregoing describes apparatus and methods for computing softinformation at a mobile station having a plurality of receive antennasin the presence of interference. Those skilled in the art willappreciate that the invention can be practiced by other than thedescribed embodiments, which are presented for the purpose ofillustration rather than of limitation.

1. A method of computing soft information for use in estimating digitalinformation from an intended source, the method comprising: estimatinginterference channel information associated with at least oneinterfering source; obtaining interference modulation informationassociated with the at least one interfering source; receiving a signalvector with a plurality of signal components, wherein the signal vectorrepresents the digital information, and wherein at least a subset of thesignal components includes an interference signal from the at least oneinterfering source; computing the soft information corresponding to thedigital information using the interference channel information and theinterference modulation information; and computing an expected receivesignal vector using the estimated interference channel information andthe interference modulation information.
 2. The method of claim 1, themethod further comprising: estimating intended channel informationassociated with the intended source, wherein computing the softinformation further comprises computing the soft information based onthe intended channel information.
 3. The method of claim 1, whereinestimating the interference channel information comprises: receiving apilot signal from each of a plurality of interfering sources includingthe at least one interfering source; and analyzing each received pilotsignal.
 4. The method of claim 1, wherein estimating the interferencechannel information comprises: computing, for each of the signalcomponents, an estimate of a channel gain associated with the at leastone interference source.
 5. The method of claim 1, the method furthercomprising: receiving control information from the at least oneinterference source; and decoding the control information to obtain theinterference modulation information.
 6. The method of claim 1, whereincomputing the soft information comprises: computing a log-likelihoodratio (LLR) for each bit of the digital information.
 7. The method ofclaim 1, wherein the expected receive signal vector is computed for aplurality of possible transmit signal vectors, each of the possibletransmit signal vectors including transmit signal components that areeach associated with one of the at least one interference source.
 8. Themethod of claim 1, wherein computing the soft information comprises:computing a Euclidean distance-based metric based on a Euclideandistance between the received signal vector and the expected receivesignal vector.
 9. The method of claim 8, the method further comprising:multiplying the expected receive signal vector by a unitary matrix,wherein computing the Euclidean distance-based metric comprisescalculating the Euclidean distance-based metric using the multipliedexpected receive signal vector.
 10. The method of claim 9, wherein theunitary matrix is obtained as a result of a QR decomposition of achannel matrix.
 11. The method of claim 1, the method furthercomprising: selecting a subset of the signal components, whereincomputing the soft information comprises calculating the softinformation using only the subset of the signal components.
 12. Themethod of claim 11, wherein selecting the subset comprises: choosingthose signal components that have a signal-to-interference plus noiseratio (SINR) of at least a predetermined value.
 13. The method of claim11, wherein selecting the subset comprises: performing an algorithm on achannel matrix that is based on a sorted QR decomposition.
 14. Themethod of claim 13, wherein performing the algorithm comprises:selecting, as a first orthogonal column vector, a unit vector having allenergy of the received signal vector that corresponds to the intendedsource.
 15. The method of claim 11, wherein computing the softinformation comprises: calculating an expected receive signal vector;and multiplying the expected receive signal vector by a modifyingmatrix, the modifying matrix having a number of columns based on anumber of signal components in the subset, wherein the columns areorthogonal.
 16. The method of claim 1, wherein the at least oneinterference source comprises a plurality of interference sources, themethod further comprising: selecting at least a subset of theinterference sources, wherein computing the soft information comprisescalculating the soft information using only the at least a subset of theinterference sources.
 17. The method of claim 16, wherein selecting theat least a subset comprises: analyzing columns of a channel matrix,where each column is associated with one of the interference sources.18. Apparatus for computing soft information to estimate digitalinformation from an intended source, the apparatus comprising:computational logic configured to estimate interference channelinformation associated with at least one interfering source; a pluralityof receive antennas operable to: obtain interference modulationinformation associated with the at least one interfering source; andreceive a signal vector with a plurality of signal components, whereinthe signal vector represents the digital information, and wherein atleast a subset of the signal components includes an interference signalfrom the at least one interfering source; and a soft bit-metriccalculator configured to: compute the soft information corresponding tothe digital information using the interference channel information andthe interference modulation information; and compute an expected receivesignal vector using the estimated interference channel information andthe interference modulation information.
 19. The apparatus of claim 18,wherein the apparatus is implemented on a mobile station, and whereinthe intended source and the at least one interfering source are basestations.
 20. The apparatus of claim 18, wherein the apparatus isimplemented on a base station, and wherein the intended source and theat least one interfering source are mobile stations.
 21. The apparatusof claim 18, wherein the computational logic is further configured toestimate intended channel information associated with the intendedsource, and wherein the soft bit-metric calculator is further configuredto compute the soft information based on the intended channelinformation.
 22. The apparatus of claim 18, wherein the computationallogic is configured to: receive a pilot signal from each of a pluralityof interfering sources including the at least one interfering source;and analyze each received pilot signal to obtain the estimate of theinterference channel information.
 23. The apparatus of claim 18, whereinthe computational logic is further configured to: compute, for each ofthe signal components, an estimate of a channel gain associated with theat least one interference source.
 24. The apparatus of claim 18, theapparatus further comprising: a control information decoder configuredto decode control information received from the at least oneinterference source.
 25. The apparatus of claim 18, wherein the softbit-metric calculator is further configured to: compute a log-likelihoodratio (LLR) for each bit of the digital information.
 26. The apparatusof claim 18, wherein the soft bit-metric calculator is furtherconfigured to: compute the expected receive signal vector for aplurality of possible transmit signal vectors, each of the possibletransmit signal vectors including transmit signal components that areeach associated with one of the at least one interference source. 27.The apparatus of claim 18, wherein the soft bit-metric calculator isfurther configured to: compute a Euclidean distance-based metric basedon a Euclidean distance between the received signal vector and theexpected receive signal vector.
 28. The apparatus of claim 27, whereinthe soft bit-metric calculator is further configured to: multiply theexpected receive signal vector by a unitary matrix; and calculate theEuclidean distance-based metric using the multiplied expected receivesignal vector.
 29. The apparatus of claim 18, wherein the softbit-metric calculator is further configured to: select a subset of thesignal components; and calculate the soft information using only thesubset of the signal components.
 30. The apparatus of claim 29, whereinthe soft bit-metric calculator is further configured to: choose, for thesubset, those signal components that have a signal-to-interference plusnoise ratio (SINR) of at least a predetermined value.
 31. The apparatusof claim 29, wherein the soft bit-metric calculator is furtherconfigured to: calculate an expected receive signal vector; and multiplythe expected receive signal vector by a modifying matrix, the modifyingmatrix having a number of columns based on a number of signal componentsin the subset, wherein the columns are orthogonal.
 32. The apparatus ofclaim 18, wherein the at least one interference source comprises aplurality of interference sources, and wherein the soft bit-metriccalculator is further configured to: select at least a subset of theinterference sources; and calculate the soft information using only theat least a subset of the interference sources.